Cited by 662Scientific Computing and Data ManagementBioinformatics and Genomic NetworksGene Regulatory Network Analysis

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Functional genomic hypothesis generation and experimentation by a robot scientist

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King, Ross D., Whelan, Kenneth E., Jones, Ffion M., Reiser, Philip G. K., Bryant, Christopher H., Muggleton, Stephen H., Kell, Douglas B., Oliver, Stephen G.·2004·Nature

DOI 10.1038/nature02236

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Abstract

The question of whether it is possible to automate the scientific process is of both great theoretical interest and increasing practical importance because, in many scientific areas, data are being generated much faster than they can be effectively analysed. We describe a physically implemented robotic system that applies techniques from artificial intelligence to carry out cycles of scientific experimentation. The system automatically originates hypotheses to explain observations, devises experiments to test these hypotheses, physically runs the experiments using a laboratory robot, interprets the results to falsify hypotheses inconsistent with the data, and then repeats the cycle. Here we apply the system to the determination of gene function using deletion mutants of yeast (Saccharomyces cerevisiae) and auxotrophic growth experiments. We built and tested a detailed logical model (involving genes, proteins and metabolites) of the aromatic amino acid synthesis pathway. In biological experiments that automatically reconstruct parts of this model, we show that an intelligent experiment selection strategy is competitive with human performance and significantly outperforms, with a cost decrease of 3-fold and 100-fold (respectively), both cheapest and random-experiment selection.

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Authors: King, R. D., Whelan, K. E., Jones, F. M., Reiser, P. G. K., Bryant, C. H., Muggleton, S. H., Kell, D. B., & Oliver, S. G.
Affiliations: Aberystwyth University (GB)
Robert Gordon University (GB)
Imperial College London (GB)
University of Manchester (GB)
Corresponding author: Oliver, Stephen G.
ORCID: King, Ross D.
Bryant, Christopher H.
Muggleton, Stephen H.
Kell, Douglas B.
Oliver, Stephen G.
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2004-01
Volume: 427
Issue: 6971
Pages: 247-252
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/nature02236
OpenAlex: W2136636278
Cited by: 662
References: 29
Citations by year: 2026: 26 · 2025: 31 · 2024: 26 · 2023: 54 · 2022: 52 · 2021: 27 · 2020: 30 · 2019: 33 · 2018: 15 · 2017: 25 · 2016: 14 · 2015: 22 · 2014: 25 · 2013: 24 · 2012: 24
FWCI: 106.81
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springer.com/tdm (tdm)
Locations: Nature (publishedVersion)
PubMed (publishedVersion)
Research Explorer (The University of Manchester) (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
University of Salford Institutional Repository (University of Salford) (submittedVersion, cc-by)
Funders: National Institute for Health and Care Research (NIHR202203)
Department of Health and Social Care
Engineering and Physical Sciences Research Council (GR/R60621/01, GR/R54071/01)
Biotechnology and Biological Sciences Research Council (E19354)
Topics: Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords: robot scientists, automated hypothesis generation, automated experimentation, laboratory automation, gene function analysis, deletion mutants, Saccharomyces cerevisiae, aromatic amino acid biosynthesis, metabolic pathway modeling, artificial intelligence in science
MeSH terms: Workforce, Algorithms, Amino Acids, Computer Simulation, Cost-Benefit Analysis, Efficiency, Genes, Fungal, Humans, Learning, Models, Biological, Phenotype, Probability, Research, Research Design, Research Personnel, Robotics, Saccharomyces cerevisiae, Software, Time Factors, Open Reading Frames, Gene Deletion, Computational Biology, Genomics, Saccharomyces cerevisiae Proteins

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Functional genomic hypothesis generation and experimentation by a robot scientist

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